When sample data is collected and then subsequently analyzed, some data is often categorized as outlier data. The outlier data in some instances may be a result of incorrect readings by measurement equipment. However, some outlier data may be accurately measured, but only for a few respondents. When the outlier data includes relatively high values, such as outlier data from a right-side tail of a distribution of data, then the outlier data may complicate an analysis of test results. The existence of the outlier data may require a test to include additional samples or may require an analyzer to manipulate the data to remove the outliers. Collection of additional samples may be time consuming and expensive. Removal of outliers may bias the results when the outliers include accurate data that is not a result of an incorrect measurement reading. For example, removal of the outlier data may result in a measured difference that is statistically significant. Thus, any benefit gained by a test or experiment may include a heavy negative bias when the outlier data is removed from the samples.